The primary goal of this application is a systems-level understanding of gene regulatory circuits. Until the recent rise of systems biology, most work on such circuits has involved identifying components and interactions, followed by progressively finer mechanistic analysis. This approach has provided qualitative descriptions of numerous gene regulatory circuits. In general, however, these descriptions do not allow one to predict the detailed behavior of the systems they schematize. This behavior arises from interactions among system components, and is often termed "systems behavior." This higher-order behavior is embodied in the statement that "the whole is more than the sum of its parts." We propose to continue a more integrative approach aimed at describing the systems behavior of a system that is relatively well understood at the mechanistic level. Without such an understanding, a description of a system in terms of causal pathways and networks remains incomplete, because such a description lacks predictive power. We will apply this approach to a well-studied prokaryotic regulatory circuit, that of E. coli bacteriophage lambda. This virus can persist in a highly stable regulatory state, the lysogenic state. It can also switch from this state to a different regulatory state in response to particular stimuli. We propose to use a combination of experimental analysis and computer simulation to derive an integrated description of this regulatory circuit. Our goal is to make a computer simulation that will describe the behavior of the wild-type and existing mutants, and will predict the behavior of new mutants. Success in this endeavor will provide a strong link between in vivo and in vitro studies, and will provide a quantitative description of the circuit that goes far beyond the descriptions available for most systems. The proposed work will also provide evidence for mechanisms by which complex regulatory circuits evolve and change their behavior during evolution. All cells regulate their behavior and respond to their environment, using specific regulatory mechanisms, which combine to give the observed behavior of the cell. Quantitative description of systems behavior is crucial for a detailed understanding, particularly in disease states in which these pathways have gone awry. It is proposed to develop such a description for a well-understood and relatively simple regulatory circuit; general principles learned from this approach can then be applied to more complex organisms.